The underlying task of this work is to build a neural audio synthesizer that maps piano performances in MIDI to audio.
The current literature either focuses on such direct mapping without additional controllability with regard to performance styles [1]}, [2]}, or concerns only the global attributes such as synthesizing different instruments [3]}.
We distinguish ourselves from the previous works by incorporating GM-VAE [4]}, which has been applied to speech [5]} and instrument modelling  [6]}, to disentangle two significant expressive performance factors. This allows us to achieve creative applications such as gradual style morphing over time.
The sequence of conditions can be sampled from the prior or inferred from other pieces, as shown in Section .
